readcon-core 0.14.5

//! Opaque numeric storage for builders and frames (metatensor v0.2.x shape).
//!
//! **Design:** internal data is DLPack-shaped (`shape` / `dtype` / `device` /
//! `as_dlpack(device, stream, max_version)`), not “AoS structs with a DLPack
//! export bolt-on.” Callers **query** storage dtype/device, then `as_dlpack`
//! with a **requested device** (and stream / max version)—same contract as
//! metatensor `mts_array_t`. Choosing f32 vs f64 is choosing **storage**
//! (or a future typed builder), not passing a cast-target on every export.
//!
//! `ConFrame` keeps row-major `ArcArray` blocks for positions and optional
//! sections; `atom_data` is the CON-text AoS projection for the writer.
//!
//! Implementors can swap dtypes (f32 / f64 / u64 / bool), devices (CPU /
//! future GPU), and ownership (`ArrayD`, `Arc<RwLock<...>>`, …) without
//! changing the public surface.
//!
//! The default backing is `Arc<RwLock<ndarray::ArrayD<T>>>` --
//!   * `Arc`     : multiple DLPack views can share the same buffer
//!     across threads / FFI consumers.
//!   * `RwLock`  : enforces aliasing soundness; concurrent reads
//!     are non-blocking, concurrent writes contend.
//!   * `ndarray::ArrayD<T>` : type-erased dimension, generic dtype,
//!     ndarray's allocator (8-byte aligned, fine for f64; future
//!     SIMD-aligned variants implement this trait separately).
//!
//! See `docs/orgmode/spec.org` §17 for the public contract.

use std::sync::{Arc, RwLock, TryLockError};

use dlpk::sys::{DLDataType, DLDevice, DLPackVersion};
use dlpk::{DLPackPointerCast, DLPackTensor, GetDLPackDataType};
use ndarray::ArrayD;

use crate::error::ParseError;

/// Storage hook for one per-atom field of a ConFrameBuilder.
///
/// Implementors hold the raw bytes for a single field (e.g. all atom
/// positions as a `(N, 3) f64` block) and expose them via DLPack so
/// downstream consumers can map a numpy / Eigen / torch view onto
/// the same memory zero-copy.
pub trait Array: std::any::Any + Send + Sync {
    /// `&dyn Any` access for downcast (mirrors metatensor's pattern).
    fn as_any(&self) -> &dyn std::any::Any;

    /// `&mut dyn Any` access for downcast.
    fn as_any_mut(&mut self) -> &mut dyn std::any::Any;

    /// Shape of the underlying tensor.
    fn shape(&self) -> Vec<usize>;

    /// DLPack dtype of the elements.
    fn dtype(&self) -> DLDataType;

    /// Device residency of the storage.
    fn device(&self) -> DLDevice;

    /// Export a DLPack-managed tensor view of this array.
    ///
    /// `device` requests the consumer's preferred device; CPU
    /// implementors return `Err(ParseError::ValidationError(...))`
    /// when asked for a non-CPU device they cannot service.
    /// `stream` is the consumer's stream (CUDA / ROCm / SYCL); CPU
    /// backings ignore it.
    fn as_dlpack(
        &self,
        device: DLDevice,
        stream: Option<i64>,
        max_version: DLPackVersion,
    ) -> Result<DLPackTensor, ParseError>;

    /// Deep-copy this array (used by ConFrameBuilder::clone +
    /// `move_data`-style ops). Default impl just `clone`s through
    /// the implementor's natural mechanism.
    fn copy(&self) -> Box<dyn Array>;
}

/// Default Rust backing for the Array trait: shared, lockable,
/// dynamic-rank ndarray. Matches metatensor v2's
/// `Arc<RwLock<ArrayD<T>>>` choice and inherits its DLPack +
/// concurrency semantics.
impl<T> Array for Arc<RwLock<ArrayD<T>>>
where
    T: 'static + Send + Sync + Clone + Default + GetDLPackDataType + DLPackPointerCast,
{
    fn as_any(&self) -> &dyn std::any::Any {
        self
    }

    fn as_any_mut(&mut self) -> &mut dyn std::any::Any {
        self
    }

    fn shape(&self) -> Vec<usize> {
        match self.try_read() {
            Ok(lock) => lock.shape().to_vec(),
            Err(TryLockError::Poisoned(_)) => panic!("readcon-core array lock is poisoned"),
            Err(TryLockError::WouldBlock) => panic!("readcon-core array is already locked"),
        }
    }

    fn dtype(&self) -> DLDataType {
        T::get_dlpack_data_type()
    }

    fn device(&self) -> DLDevice {
        DLDevice::cpu()
    }

    fn as_dlpack(
        &self,
        device: DLDevice,
        _stream: Option<i64>,
        _max_version: DLPackVersion,
    ) -> Result<DLPackTensor, ParseError> {
        if device != DLDevice::cpu() {
            return Err(ParseError::ValidationError(format!(
                "Arc<RwLock<ArrayD>> is CPU-only; requested device {device:?} unsupported"
            )));
        }
        // Borrow the inner ArrayD<T> read-only and convert to DLPack
        // through dlpk's ndarray feature. The resulting DLPackTensor
        // owns a clone of the Arc, so the lifetime is decoupled from
        // the borrow above.
        let lock = match self.try_read() {
            Ok(lock) => lock,
            Err(TryLockError::Poisoned(_)) => {
                return Err(ParseError::ValidationError(
                    "readcon-core array lock is poisoned".into(),
                ));
            }
            Err(TryLockError::WouldBlock) => {
                return Err(ParseError::ValidationError(
                    "readcon-core array is already locked".into(),
                ));
            }
        };
        // Clone the ArrayD<T> contents into an owned ndarray, then
        // hand it to dlpk's TryFrom<ArrayD<T>> -> DLPackTensor (this
        // takes ownership, so the resulting DLPackTensor has its own
        // backing storage independent of the Arc<RwLock<...>>; future
        // optimisation: build a custom Array impl that exposes the
        // Arc-shared storage directly via dlpk's manager_ctx).
        let owned: ArrayD<T> = lock.to_owned();
        DLPackTensor::try_from(owned).map_err(|e| {
            ParseError::ValidationError(format!("dlpk ArrayD conversion failed: {e}"))
        })
    }

    fn copy(&self) -> Box<dyn Array> {
        // Cheap Arc clone, NOT a deep copy of the data buffer. If a
        // caller needs a true deep copy, materialize via
        // `Arc::new(RwLock::new(ArrayD::clone(&*lock)))`.
        Box::new(Arc::clone(self))
    }
}

/// Convenience constructor for the default backing.
pub fn array_from_shape<T>(shape: &[usize]) -> Box<dyn Array>
where
    T: 'static + Send + Sync + Clone + Default + GetDLPackDataType + DLPackPointerCast,
{
    let arr: ArrayD<T> = ArrayD::default(ndarray::IxDyn(shape));
    Box::new(Arc::new(RwLock::new(arr)))
}

/// Allocate a new zeroed array **on** `device`.
///
/// Default builds only allocate on CPU. Non-CPU allocation fails with a clear
/// error (not silent reinterpret as CPU). Callers that already own device
/// memory should use [`array_from_host_f64_on_device`] / [`from_dlpack_f64`] to
/// **preserve** device identity without allocating on the GPU in this process.
pub fn allocate_array_on_device(
    shape: &[usize],
    device: DLDevice,
) -> Result<Box<dyn Array>, ParseError> {
    if device == DLDevice::cpu() {
        return Ok(array_from_shape::<f64>(shape));
    }
    #[cfg(feature = "cuda")]
    {
        use dlpk::sys::DLDeviceType;
        if device.device_type == DLDeviceType::kDLCUDA {
            return crate::cuda_array::allocate_cuda_f64(shape, device.device_id);
        }
    }
    Err(ParseError::ValidationError(format!(
        "no device allocator in this build for {device:?}; use caller-supplied device buffers via from_dlpack / array_from_host_f64_on_device, or build with `--features cuda` for CUDA devices"
    )))
}

/// Host-resident `f64` buffer tagged with a DLPack `device` (CPU or non-CPU).
///
/// Used for **device-preserving interchange**: callers (or tests) supply
/// buffers that logically live on CUDA/ROCm/etc. without requiring a CUDA
/// driver in the default build. [`Array::device`] and matching
/// [`Array::as_dlpack`] requests preserve the tag; mismatched device requests
/// fail without `--features cuda`; with that feature, CUDA allocate uses real
/// device memory (see [`crate::cuda_array`]).
pub struct DeviceTaggedF64Array {
    shape: Vec<usize>,
    device: DLDevice,
    /// Row-major elements (host-visible for contract tests / zero-copy tag).
    data: Arc<Vec<f64>>,
}

impl DeviceTaggedF64Array {
    /// Build from host `f64` values with an explicit DLPack device tag.
    pub fn new(shape: &[usize], data: Vec<f64>, device: DLDevice) -> Result<Self, ParseError> {
        let n: usize = shape.iter().product();
        if data.len() != n {
            return Err(ParseError::ValidationError(format!(
                "device-tagged array: expected {n} f64 values for shape {shape:?}, got {}",
                data.len()
            )));
        }
        Ok(Self {
            shape: shape.to_vec(),
            device,
            data: Arc::new(data),
        })
    }
}

/// Install a device-tagged f64 array (caller-supplied buffer / logical device).
pub fn array_from_host_f64_on_device(
    shape: &[usize],
    data: Vec<f64>,
    device: DLDevice,
) -> Result<Box<dyn Array>, ParseError> {
    Ok(Box::new(DeviceTaggedF64Array::new(shape, data, device)?))
}

/// Ingest from a DLPack tensor: preserve `tensor.device()` and copy f64 host
/// elements when the tensor is CPU-addressable; for non-CPU tensors in this
/// build we still preserve the **device tag** using host staging only when the
/// tensor reports f64 data that is readable (host tests tag CUDA with host
/// bytes). Pure allocate-on-GPU without a backend is [`allocate_array_on_device`].
pub fn from_dlpack_f64(tensor: &DLPackTensor) -> Result<Box<dyn Array>, ParseError> {
    let device = tensor.device();
    let shape: Vec<usize> = tensor.shape().iter().map(|&d| d as usize).collect();
    let n: usize = shape.iter().product();
    let dtype = tensor.dtype();
    if dtype.code != dlpk::sys::DLDataTypeCode::kDLFloat || dtype.bits != 64 {
        return Err(ParseError::ValidationError(format!(
            "from_dlpack_f64: expected f64, got dtype code={:?} bits={}",
            dtype.code, dtype.bits
        )));
    }
    // Read elements via data pointer (host-backed tensors and host-staged
    // device-tagged tests). Non-readable device memory would fail here.
    let ptr = tensor
        .data_ptr::<f64>()
        .map_err(|e| ParseError::ValidationError(format!("from_dlpack_f64 data_ptr: {e}")))?;
    let mut data = vec![0.0f64; n];
    if n > 0 {
        unsafe {
            std::ptr::copy_nonoverlapping(ptr, data.as_mut_ptr(), n);
        }
    }
    array_from_host_f64_on_device(&shape, data, device)
}

struct DeviceTaggedManager {
    data: Arc<Vec<f64>>,
    shape: Vec<i64>,
}

unsafe extern "C" fn device_tagged_deleter(managed: *mut dlpk::sys::DLManagedTensorVersioned) {
    if managed.is_null() {
        return;
    }
    // Only free our manager_ctx. When constructed via `DLPackTensor::from_raw`,
    // the outer dlpk deleter restores `manager_ctx`/`deleter` then calls us and
    // finally frees the managed tensor allocation — we must not free `managed`.
    unsafe {
        let ctx = (*managed).manager_ctx;
        if !ctx.is_null() {
            let _ = Box::from_raw(ctx as *mut DeviceTaggedManager);
            (*managed).manager_ctx = std::ptr::null_mut();
        }
    }
}

impl Array for DeviceTaggedF64Array {
    fn as_any(&self) -> &dyn std::any::Any {
        self
    }

    fn as_any_mut(&mut self) -> &mut dyn std::any::Any {
        self
    }

    fn shape(&self) -> Vec<usize> {
        self.shape.clone()
    }

    fn dtype(&self) -> DLDataType {
        f64::get_dlpack_data_type()
    }

    fn device(&self) -> DLDevice {
        self.device
    }

    fn as_dlpack(
        &self,
        device: DLDevice,
        _stream: Option<i64>,
        _max_version: DLPackVersion,
    ) -> Result<DLPackTensor, ParseError> {
        if device != self.device {
            return Err(ParseError::ValidationError(format!(
                "device mismatch: array is on {:?}, requested {:?}",
                self.device, device
            )));
        }
        // Build a managed tensor that reports `self.device` while holding an
        // Arc to host-resident f64 storage (device-tag preserving interchange
        // without a CUDA allocator in the default build).
        let manager = Box::new(DeviceTaggedManager {
            data: Arc::clone(&self.data),
            shape: self.shape.iter().map(|&d| d as i64).collect(),
        });
        let data_ptr = manager.data.as_ptr() as *mut std::ffi::c_void;
        let shape_ptr = manager.shape.as_ptr() as *mut i64;
        let mut managed = dlpk::sys::DLManagedTensorVersioned {
            version: dlpk::sys::DLPackVersion {
                major: dlpk::sys::DLPACK_MAJOR_VERSION,
                minor: dlpk::sys::DLPACK_MINOR_VERSION,
            },
            manager_ctx: std::ptr::null_mut(),
            deleter: Some(device_tagged_deleter),
            dl_tensor: dlpk::sys::DLTensor {
                data: data_ptr,
                device: self.device,
                ndim: self.shape.len() as i32,
                dtype: f64::get_dlpack_data_type(),
                shape: shape_ptr,
                strides: std::ptr::null_mut(),
                byte_offset: 0,
            },
            flags: 0,
        };
        managed.manager_ctx = Box::into_raw(manager) as *mut std::ffi::c_void;
        // Safety: valid DLManagedTensorVersioned; deleter frees DeviceTaggedManager
        // (Arc + shape storage). from_raw re-wraps with a Rust manager that
        // still invokes our deleter.
        Ok(unsafe { DLPackTensor::from_raw(managed) })
    }

    fn copy(&self) -> Box<dyn Array> {
        Box::new(Self {
            shape: self.shape.clone(),
            device: self.device,
            data: Arc::clone(&self.data),
        })
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn array_from_shape_reports_shape_and_dtype() {
        let a: Box<dyn Array> = array_from_shape::<f64>(&[5, 3]);
        assert_eq!(a.shape(), vec![5, 3]);
        let dt = a.dtype();
        assert_eq!(dt.code, dlpk::sys::DLDataTypeCode::kDLFloat);
        assert_eq!(dt.bits, 64);
        assert_eq!(dt.lanes, 1);
        assert_eq!(a.device(), DLDevice::cpu());
    }

    #[test]
    fn array_copy_shares_storage_via_arc() {
        let a = array_from_shape::<f64>(&[2, 3]);
        let b = a.copy();
        // shapes match
        assert_eq!(a.shape(), b.shape());
    }

    #[test]
    fn array_dlpack_export_round_trip() {
        let a = array_from_shape::<f64>(&[4, 3]);
        let tensor = a
            .as_dlpack(DLDevice::cpu(), None, DLPackVersion::current())
            .expect("DLPack export should succeed for CPU array");
        assert_eq!(tensor.shape(), &[4, 3]);
    }

    /// Without `--features cuda`, non-CPU allocate must fail with a clear error
    /// (never panic inside a missing driver loader).
    #[cfg(not(feature = "cuda"))]
    #[test]
    fn allocate_non_cpu_fails_clearly() {
        match allocate_array_on_device(&[2, 3], DLDevice::cuda(0)) {
            Ok(_) => panic!("non-CPU allocate must fail without --features cuda"),
            Err(err) => {
                let msg = format!("{err:?}");
                assert!(
                    msg.contains("no device allocator") || msg.contains("allocator"),
                    "{msg}"
                );
            }
        }
        let cpu = allocate_array_on_device(&[2, 3], DLDevice::cpu()).unwrap();
        assert_eq!(cpu.device(), DLDevice::cpu());
    }

    /// With `--features cuda` and a working driver, allocate on CUDA must succeed
    /// and report `kDLCUDA` (real device memory path).
    #[cfg(feature = "cuda")]
    #[test]
    fn allocate_cuda_succeeds_with_feature() {
        let a = allocate_array_on_device(&[2, 3], DLDevice::cuda(0))
            .expect("CUDA allocate must succeed with --features cuda and a driver");
        assert_eq!(a.device(), DLDevice::cuda(0));
        assert_eq!(
            a.device().device_type,
            dlpk::sys::DLDeviceType::kDLCUDA
        );
        let t = a
            .as_dlpack(DLDevice::cuda(0), None, DLPackVersion::current())
            .expect("matching as_dlpack");
        assert_eq!(t.device().device_type, dlpk::sys::DLDeviceType::kDLCUDA);
        let cpu = allocate_array_on_device(&[2, 3], DLDevice::cpu()).unwrap();
        assert_eq!(cpu.device(), DLDevice::cpu());
    }

    #[test]
    fn cuda_tagged_preserves_device_and_matching_as_dlpack() {
        let data: Vec<f64> = (0..6).map(|i| i as f64).collect();
        let a = array_from_host_f64_on_device(&[2, 3], data.clone(), DLDevice::cuda(0)).unwrap();
        assert_eq!(a.device(), DLDevice::cuda(0));
        assert_eq!(a.shape(), vec![2, 3]);

        let mismatch = a
            .as_dlpack(DLDevice::cpu(), None, DLPackVersion::current())
            .unwrap_err();
        assert!(
            format!("{mismatch:?}").contains("device mismatch"),
            "{mismatch:?}"
        );

        let tensor = a
            .as_dlpack(DLDevice::cuda(0), None, DLPackVersion::current())
            .expect("matching CUDA device export");
        assert_eq!(tensor.device(), DLDevice::cuda(0));
        assert_eq!(tensor.shape(), &[2, 3]);

        // from_dlpack preserves device tag
        let back = from_dlpack_f64(&tensor).unwrap();
        assert_eq!(back.device(), DLDevice::cuda(0));
        assert_eq!(back.shape(), vec![2, 3]);
        let again = back
            .as_dlpack(DLDevice::cuda(0), None, DLPackVersion::current())
            .unwrap();
        assert_eq!(again.device(), DLDevice::cuda(0));
    }

    #[test]
    fn cpu_tagged_path_unchanged() {
        let a = array_from_host_f64_on_device(&[1, 3], vec![1.0, 2.0, 3.0], DLDevice::cpu()).unwrap();
        assert_eq!(a.device(), DLDevice::cpu());
        let t = a
            .as_dlpack(DLDevice::cpu(), None, DLPackVersion::current())
            .unwrap();
        assert_eq!(t.device(), DLDevice::cpu());
        let back = from_dlpack_f64(&t).unwrap();
        assert_eq!(back.device(), DLDevice::cpu());
    }
}